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<div class="title">sac.h</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Software License Agreement (BSD License)</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *  Copyright (c) 2010-2011, Willow Garage, Inc.</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> *  Copyright (c) 2012-, Open Perception, Inc.</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> *  All rights reserved. </span></div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> *  Redistribution and use in source and binary forms, with or without</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> *  modification, are permitted provided that the following conditions</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *  are met:</span></div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> *   * Redistributions of source code must retain the above copyright</span></div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> *     notice, this list of conditions and the following disclaimer.</span></div>
<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160;<span class="comment"> *   * Redistributions in binary form must reproduce the above</span></div>
<div class="line"><a name="l00017"></a><span class="lineno">   17</span>&#160;<span class="comment"> *     copyright notice, this list of conditions and the following</span></div>
<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> *     disclaimer in the documentation and/or other materials provided</span></div>
<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> *     with the distribution.</span></div>
<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> *   * Neither the name of the copyright holder(s) nor the names of its</span></div>
<div class="line"><a name="l00021"></a><span class="lineno">   21</span>&#160;<span class="comment"> *     contributors may be used to endorse or promote products derived</span></div>
<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> *     from this software without specific prior written permission.</span></div>
<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="comment"> *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS</span></div>
<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160;<span class="comment"> *  &quot;AS IS&quot; AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT</span></div>
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<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="comment"> *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER</span></div>
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<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;<span class="comment"> *  POSSIBILITY OF SUCH DAMAGE.</span></div>
<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="comment"> * $Id$</span></div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160; </div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#ifndef PCL_SAMPLE_CONSENSUS_H_</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;<span class="preprocessor">#define PCL_SAMPLE_CONSENSUS_H_</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160; </div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="preprocessor">#include &lt;pcl/sample_consensus/boost.h&gt;</span></div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">#include &lt;pcl/sample_consensus/sac_model.h&gt;</span></div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="preprocessor">#include &lt;ctime&gt;</span></div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;<span class="preprocessor">#include &lt;set&gt;</span></div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160; </div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="keyword">namespace </span>pcl</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;{</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> T&gt;</div>
<div class="line"><a name="l00056"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html">   56</a></span>&#160;  <span class="keyword">class </span><a class="code" href="classpcl_1_1_sample_consensus.html">SampleConsensus</a></div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;  {</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;    <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code" href="classpcl_1_1_sample_consensus_model.html">SampleConsensusModel&lt;T&gt;::Ptr</a> SampleConsensusModelPtr;</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160; </div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    <span class="keyword">private</span>:</div>
<div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#ad113bbc7fe758479a315bc5108324336">   62</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#ad113bbc7fe758479a315bc5108324336">SampleConsensus</a> () {};</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160; </div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;    <span class="keyword">public</span>:</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;      <span class="keyword">typedef</span> boost::shared_ptr&lt;SampleConsensus&gt; Ptr;</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;      <span class="keyword">typedef</span> boost::shared_ptr&lt;const SampleConsensus&gt; ConstPtr;</div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160; </div>
<div class="line"><a name="l00072"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#aee8d85e0b1062f5e18d43609e6ac59bf">   72</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#aee8d85e0b1062f5e18d43609e6ac59bf">SampleConsensus</a> (<span class="keyword">const</span> SampleConsensusModelPtr &amp;model, <span class="keywordtype">bool</span> random = <span class="keyword">false</span>) </div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;        : <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a> (model)</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#a0e04da16522ae180cb8cc2e6ef0d2244">model_</a> ()</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#a0115926eadf78f7bc1ad4675659d8343">inliers_</a> ()</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#a96f852dfca500689684313d3cb7f84b1">model_coefficients_</a> ()</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#a025913cc2a2099a553fe7842aa792326">probability_</a> (0.99)</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#a471e062f42e9cb4ae9d77107cc135acb">iterations_</a> (0)</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">threshold_</a> (std::numeric_limits&lt;double&gt;::max ())</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">max_iterations_</a> (1000)</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#a3860965324830148970ba99223663aa2">rng_alg_</a> ()</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#aa23f804b4957312659adca2068e05682">rng_</a> (new boost::uniform_01&lt;boost::mt19937&gt; (<a class="code" href="classpcl_1_1_sample_consensus.html#a3860965324830148970ba99223663aa2">rng_alg_</a>))</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;      {</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;         <span class="comment">// Create a random number generator object</span></div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;         <span class="keywordflow">if</span> (random)</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;           <a class="code" href="classpcl_1_1_sample_consensus.html#aa23f804b4957312659adca2068e05682">rng_</a>-&gt;base ().seed (<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span><span class="keyword">&gt;</span> (std::time (0)));</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;         <span class="keywordflow">else</span></div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;           <a class="code" href="classpcl_1_1_sample_consensus.html#aa23f804b4957312659adca2068e05682">rng_</a>-&gt;base ().seed (12345u);</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;      };</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160; </div>
<div class="line"><a name="l00096"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#aac4937e9bc2a8acf15ae97c7d763090a">   96</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#aac4937e9bc2a8acf15ae97c7d763090a">SampleConsensus</a> (<span class="keyword">const</span> SampleConsensusModelPtr &amp;model, </div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;                       <span class="keywordtype">double</span> threshold, </div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;                       <span class="keywordtype">bool</span> random = <span class="keyword">false</span>)</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;        : <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a> (model)</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#a0e04da16522ae180cb8cc2e6ef0d2244">model_</a> ()</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#a0115926eadf78f7bc1ad4675659d8343">inliers_</a> ()</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#a96f852dfca500689684313d3cb7f84b1">model_coefficients_</a> ()</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#a025913cc2a2099a553fe7842aa792326">probability_</a> (0.99)</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#a471e062f42e9cb4ae9d77107cc135acb">iterations_</a> (0)</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">threshold_</a> (threshold)</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">max_iterations_</a> (1000)</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#a3860965324830148970ba99223663aa2">rng_alg_</a> ()</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;        , <a class="code" href="classpcl_1_1_sample_consensus.html#aa23f804b4957312659adca2068e05682">rng_</a> (new boost::uniform_01&lt;boost::mt19937&gt; (<a class="code" href="classpcl_1_1_sample_consensus.html#a3860965324830148970ba99223663aa2">rng_alg_</a>))</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;      {</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;         <span class="comment">// Create a random number generator object</span></div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;         <span class="keywordflow">if</span> (random)</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;           <a class="code" href="classpcl_1_1_sample_consensus.html#aa23f804b4957312659adca2068e05682">rng_</a>-&gt;base ().seed (<span class="keyword">static_cast&lt;</span><span class="keywordtype">unsigned</span><span class="keyword">&gt;</span> (std::time (0)));</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;         <span class="keywordflow">else</span></div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;           <a class="code" href="classpcl_1_1_sample_consensus.html#aa23f804b4957312659adca2068e05682">rng_</a>-&gt;base ().seed (12345u);</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;      };</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160; </div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;      <span class="keywordtype">void</span></div>
<div class="line"><a name="l00121"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#aca6f09bf3c664bfed2ae36e81909e9f2">  121</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#aca6f09bf3c664bfed2ae36e81909e9f2">setSampleConsensusModel</a> (<span class="keyword">const</span> SampleConsensusModelPtr &amp;model)</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;      {</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;        <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a> = model;</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;      }</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160; </div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;      SampleConsensusModelPtr</div>
<div class="line"><a name="l00128"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a1a101dfdcc9098f463db135a7655a438">  128</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#a1a101dfdcc9098f463db135a7655a438">getSampleConsensusModel</a> ()<span class="keyword"> const</span></div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;<span class="keyword">      </span>{</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;        <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>);</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;      }</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160; </div>
<div class="line"><a name="l00134"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a138ae225b2d724491a7abdfcfd2b4de5">  134</a></span>&#160;      <span class="keyword">virtual</span> <a class="code" href="classpcl_1_1_sample_consensus.html#a138ae225b2d724491a7abdfcfd2b4de5">~SampleConsensus</a> () {};</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160; </div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00140"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#ae1a06ccc992dfc9e65e70f5876f3c8d3">  140</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#ae1a06ccc992dfc9e65e70f5876f3c8d3">setDistanceThreshold</a> (<span class="keywordtype">double</span> threshold)  { <a class="code" href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">threshold_</a> = threshold; }</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160; </div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">double</span> </div>
<div class="line"><a name="l00144"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a575ab4a3facfdc8e007f427a96798d7d">  144</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#a575ab4a3facfdc8e007f427a96798d7d">getDistanceThreshold</a> () { <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">threshold_</a>); }</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160; </div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00150"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#af8558bc2462b6da4a2f88b2efc1ad571">  150</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#af8558bc2462b6da4a2f88b2efc1ad571">setMaxIterations</a> (<span class="keywordtype">int</span> max_iterations) { <a class="code" href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">max_iterations_</a> = max_iterations; }</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160; </div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">int</span> </div>
<div class="line"><a name="l00154"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#aa5c7f23a52dc184d8cc56790d63d375a">  154</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#aa5c7f23a52dc184d8cc56790d63d375a">getMaxIterations</a> () { <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">max_iterations_</a>); }</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160; </div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00161"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#acd6b8031622746d8b6aada91ffbaa7ee">  161</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#acd6b8031622746d8b6aada91ffbaa7ee">setProbability</a> (<span class="keywordtype">double</span> probability) { <a class="code" href="classpcl_1_1_sample_consensus.html#a025913cc2a2099a553fe7842aa792326">probability_</a> = probability; }</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160; </div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">double</span> </div>
<div class="line"><a name="l00165"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#afafb66faf0cbfa464cd884127bafdac3">  165</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#afafb66faf0cbfa464cd884127bafdac3">getProbability</a> () { <span class="keywordflow">return</span> (<a class="code" href="classpcl_1_1_sample_consensus.html#a025913cc2a2099a553fe7842aa792326">probability_</a>); }</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160; </div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;      <span class="keyword">virtual</span> <span class="keywordtype">bool</span> </div>
<div class="line"><a name="l00169"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a6bb9db27c2f0226aaa1e0c2af2b3439e">  169</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#a6bb9db27c2f0226aaa1e0c2af2b3439e">computeModel</a> (<span class="keywordtype">int</span> debug_verbosity_level = 0) = 0;</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160; </div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;      <span class="keyword">virtual</span> <span class="keywordtype">bool</span> </div>
<div class="line"><a name="l00179"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#af7c059e9ee5b5180bb7fb02b0d947c36">  179</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#af7c059e9ee5b5180bb7fb02b0d947c36">refineModel</a> (<span class="keyword">const</span> <span class="keywordtype">double</span> sigma = 3.0, <span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> max_iterations = 1000)</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;      {</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;        <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>)</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;        {</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;          PCL_ERROR (<span class="stringliteral">&quot;[pcl::SampleConsensus::refineModel] Critical error: NULL model!\n&quot;</span>);</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;          <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;        }</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160; </div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;        <span class="keywordtype">double</span> inlier_distance_threshold_sqr = <a class="code" href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">threshold_</a> * <a class="code" href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">threshold_</a>, </div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;               error_threshold = <a class="code" href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">threshold_</a>;</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;        <span class="keywordtype">double</span> sigma_sqr = sigma * sigma;</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;        <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> refine_iterations = 0;</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;        <span class="keywordtype">bool</span> inlier_changed = <span class="keyword">false</span>, oscillating = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;        std::vector&lt;int&gt; new_inliers, prev_inliers = <a class="code" href="classpcl_1_1_sample_consensus.html#a0115926eadf78f7bc1ad4675659d8343">inliers_</a>;</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;        std::vector&lt;size_t&gt; inliers_sizes;</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;        Eigen::VectorXf new_model_coefficients = <a class="code" href="classpcl_1_1_sample_consensus.html#a96f852dfca500689684313d3cb7f84b1">model_coefficients_</a>;</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;        <span class="keywordflow">do</span></div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;        {</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;          <span class="comment">// Optimize the model coefficients</span></div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;          <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>-&gt;optimizeModelCoefficients (prev_inliers, new_model_coefficients, new_model_coefficients);</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;          inliers_sizes.push_back (prev_inliers.size ());</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160; </div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;          <span class="comment">// Select the new inliers based on the optimized coefficients and new threshold</span></div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160;          <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>-&gt;selectWithinDistance (new_model_coefficients, error_threshold, new_inliers);</div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;          PCL_DEBUG (<span class="stringliteral">&quot;[pcl::SampleConsensus::refineModel] Number of inliers found (before/after): %lu/%lu, with an error threshold of %g.\n&quot;</span>, prev_inliers.size (), new_inliers.size (), error_threshold);</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;        </div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;          <span class="keywordflow">if</span> (new_inliers.empty ())</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;          {</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;            refine_iterations++;</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;            <span class="keywordflow">if</span> (refine_iterations &gt;= max_iterations)</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;              <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;            <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;            <span class="comment">//return (false);</span></div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;          }</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160; </div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;          <span class="comment">// Estimate the variance and the new threshold</span></div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;          <span class="keywordtype">double</span> variance = <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>-&gt;computeVariance ();</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;          error_threshold = sqrt (std::min (inlier_distance_threshold_sqr, sigma_sqr * variance));</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160; </div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;          PCL_DEBUG (<span class="stringliteral">&quot;[pcl::SampleConsensus::refineModel] New estimated error threshold: %g on iteration %d out of %d.\n&quot;</span>, error_threshold, refine_iterations, max_iterations);</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;          inlier_changed = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;          std::swap (prev_inliers, new_inliers);</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;          <span class="comment">// If the number of inliers changed, then we are still optimizing</span></div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;          <span class="keywordflow">if</span> (new_inliers.size () != prev_inliers.size ())</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;          {</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;            <span class="comment">// Check if the number of inliers is oscillating in between two values</span></div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;            <span class="keywordflow">if</span> (inliers_sizes.size () &gt;= 4)</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;            {</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;              <span class="keywordflow">if</span> (inliers_sizes[inliers_sizes.size () - 1] == inliers_sizes[inliers_sizes.size () - 3] &amp;&amp;</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;                  inliers_sizes[inliers_sizes.size () - 2] == inliers_sizes[inliers_sizes.size () - 4])</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;              {</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;                oscillating = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;                <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;              }</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;            }</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;            inlier_changed = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;            <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;          }</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160; </div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;          <span class="comment">// Check the values of the inlier set</span></div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; prev_inliers.size (); ++i)</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;          {</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;            <span class="comment">// If the value of the inliers changed, then we are still optimizing</span></div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;            <span class="keywordflow">if</span> (prev_inliers[i] != new_inliers[i])</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;            {</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;              inlier_changed = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;              <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;            }</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;          }</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        }</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        <span class="keywordflow">while</span> (inlier_changed &amp;&amp; ++refine_iterations &lt; max_iterations);</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;      </div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        <span class="comment">// If the new set of inliers is empty, we didn&#39;t do a good job refining</span></div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160;        <span class="keywordflow">if</span> (new_inliers.empty ())</div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;        {</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;          PCL_ERROR (<span class="stringliteral">&quot;[pcl::SampleConsensus::refineModel] Refinement failed: got an empty set of inliers!\n&quot;</span>);</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;          <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;        }</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160; </div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;        <span class="keywordflow">if</span> (oscillating)</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;        {</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;          PCL_DEBUG (<span class="stringliteral">&quot;[pcl::SampleConsensus::refineModel] Detected oscillations in the model refinement.\n&quot;</span>);</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;          <span class="keywordflow">return</span> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;        }</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160; </div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;        <span class="comment">// If no inliers have been changed anymore, then the refinement was successful</span></div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;        <span class="keywordflow">if</span> (!inlier_changed)</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;        {</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;          std::swap (<a class="code" href="classpcl_1_1_sample_consensus.html#a0115926eadf78f7bc1ad4675659d8343">inliers_</a>, new_inliers);</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;          <a class="code" href="classpcl_1_1_sample_consensus.html#a96f852dfca500689684313d3cb7f84b1">model_coefficients_</a> = new_model_coefficients;</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;          <span class="keywordflow">return</span> (<span class="keyword">true</span>);</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;        }</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;        <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;      }</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160; </div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span></div>
<div class="line"><a name="l00280"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a56b0649ebe9cd4b8a48442f864f5e83c">  280</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#a56b0649ebe9cd4b8a48442f864f5e83c">getRandomSamples</a> (<span class="keyword">const</span> boost::shared_ptr &lt;std::vector&lt;int&gt; &gt; &amp;indices, </div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;                        <span class="keywordtype">size_t</span> nr_samples, </div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;                        std::set&lt;int&gt; &amp;indices_subset)</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;      {</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;        indices_subset.clear ();</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;        <span class="keywordflow">while</span> (indices_subset.size () &lt; nr_samples)</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;          <span class="comment">//indices_subset.insert ((*indices)[(int) (indices-&gt;size () * (rand () / (RAND_MAX + 1.0)))]);</span></div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;          indices_subset.insert ((*indices)[<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (<span class="keyword">static_cast&lt;</span><span class="keywordtype">double</span><span class="keyword">&gt;</span>(indices-&gt;size ()) * <a class="code" href="classpcl_1_1_sample_consensus.html#a7a731d68a379a0a6442deae93e85d3a8">rnd</a> ())]);</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;      }</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160; </div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00294"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#ae09f01cda7605910955b0aee847ea849">  294</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#ae09f01cda7605910955b0aee847ea849">getModel</a> (std::vector&lt;int&gt; &amp;model) { model = <a class="code" href="classpcl_1_1_sample_consensus.html#a0e04da16522ae180cb8cc2e6ef0d2244">model_</a>; }</div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160; </div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00300"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a7ac2013afb3a2feaaeb661f3aa3ccf6b">  300</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#a7ac2013afb3a2feaaeb661f3aa3ccf6b">getInliers</a> (std::vector&lt;int&gt; &amp;inliers) { inliers = <a class="code" href="classpcl_1_1_sample_consensus.html#a0115926eadf78f7bc1ad4675659d8343">inliers_</a>; }</div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160; </div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">void</span> </div>
<div class="line"><a name="l00306"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a9f55f89ee72539f66f7edc8bcf6ce0c2">  306</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#a9f55f89ee72539f66f7edc8bcf6ce0c2">getModelCoefficients</a> (Eigen::VectorXf &amp;model_coefficients) { model_coefficients = <a class="code" href="classpcl_1_1_sample_consensus.html#a96f852dfca500689684313d3cb7f84b1">model_coefficients_</a>; }</div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160; </div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;    <span class="keyword">protected</span>:</div>
<div class="line"><a name="l00310"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">  310</a></span>&#160;      SampleConsensusModelPtr <a class="code" href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">sac_model_</a>;</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160; </div>
<div class="line"><a name="l00313"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a0e04da16522ae180cb8cc2e6ef0d2244">  313</a></span>&#160;      std::vector&lt;int&gt; <a class="code" href="classpcl_1_1_sample_consensus.html#a0e04da16522ae180cb8cc2e6ef0d2244">model_</a>;</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160; </div>
<div class="line"><a name="l00316"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a0115926eadf78f7bc1ad4675659d8343">  316</a></span>&#160;      std::vector&lt;int&gt; <a class="code" href="classpcl_1_1_sample_consensus.html#a0115926eadf78f7bc1ad4675659d8343">inliers_</a>;</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160; </div>
<div class="line"><a name="l00319"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a96f852dfca500689684313d3cb7f84b1">  319</a></span>&#160;      Eigen::VectorXf <a class="code" href="classpcl_1_1_sample_consensus.html#a96f852dfca500689684313d3cb7f84b1">model_coefficients_</a>;</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160; </div>
<div class="line"><a name="l00322"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a025913cc2a2099a553fe7842aa792326">  322</a></span>&#160;      <span class="keywordtype">double</span> <a class="code" href="classpcl_1_1_sample_consensus.html#a025913cc2a2099a553fe7842aa792326">probability_</a>;</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160; </div>
<div class="line"><a name="l00325"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a471e062f42e9cb4ae9d77107cc135acb">  325</a></span>&#160;      <span class="keywordtype">int</span> <a class="code" href="classpcl_1_1_sample_consensus.html#a471e062f42e9cb4ae9d77107cc135acb">iterations_</a>;</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;      </div>
<div class="line"><a name="l00328"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">  328</a></span>&#160;      <span class="keywordtype">double</span> <a class="code" href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">threshold_</a>;</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;      </div>
<div class="line"><a name="l00331"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">  331</a></span>&#160;      <span class="keywordtype">int</span> <a class="code" href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">max_iterations_</a>;</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160; </div>
<div class="line"><a name="l00334"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a3860965324830148970ba99223663aa2">  334</a></span>&#160;      boost::mt19937 <a class="code" href="classpcl_1_1_sample_consensus.html#a3860965324830148970ba99223663aa2">rng_alg_</a>;</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160; </div>
<div class="line"><a name="l00337"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#aa23f804b4957312659adca2068e05682">  337</a></span>&#160;      boost::shared_ptr&lt;boost::uniform_01&lt;boost::mt19937&gt; &gt; <a class="code" href="classpcl_1_1_sample_consensus.html#aa23f804b4957312659adca2068e05682">rng_</a>;</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160; </div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;      <span class="keyword">inline</span> <span class="keywordtype">double</span></div>
<div class="line"><a name="l00341"></a><span class="lineno"><a class="line" href="classpcl_1_1_sample_consensus.html#a7a731d68a379a0a6442deae93e85d3a8">  341</a></span>&#160;      <a class="code" href="classpcl_1_1_sample_consensus.html#a7a731d68a379a0a6442deae93e85d3a8">rnd</a> ()</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;      {</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;        <span class="keywordflow">return</span> ((*<a class="code" href="classpcl_1_1_sample_consensus.html#aa23f804b4957312659adca2068e05682">rng_</a>) ());</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;      }</div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;   };</div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;}</div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160; </div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;<span class="preprocessor">#endif  </span><span class="comment">//#ifndef PCL_SAMPLE_CONSENSUS_H_</span></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html">pcl::SampleConsensus</a></div><div class="ttdoc">SampleConsensus represents the base class. All sample consensus methods must inherit from this class.</div><div class="ttdef"><b>Definition:</b> sac.h:57</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a0115926eadf78f7bc1ad4675659d8343"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a0115926eadf78f7bc1ad4675659d8343">pcl::SampleConsensus::inliers_</a></div><div class="ttdeci">std::vector&lt; int &gt; inliers_</div><div class="ttdoc">The indices of the points that were chosen as inliers after the last computeModel () call.</div><div class="ttdef"><b>Definition:</b> sac.h:316</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a025913cc2a2099a553fe7842aa792326"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a025913cc2a2099a553fe7842aa792326">pcl::SampleConsensus::probability_</a></div><div class="ttdeci">double probability_</div><div class="ttdoc">Desired probability of choosing at least one sample free from outliers.</div><div class="ttdef"><b>Definition:</b> sac.h:322</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a0e04da16522ae180cb8cc2e6ef0d2244"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a0e04da16522ae180cb8cc2e6ef0d2244">pcl::SampleConsensus::model_</a></div><div class="ttdeci">std::vector&lt; int &gt; model_</div><div class="ttdoc">The model found after the last computeModel () as point cloud indices.</div><div class="ttdef"><b>Definition:</b> sac.h:313</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a138ae225b2d724491a7abdfcfd2b4de5"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a138ae225b2d724491a7abdfcfd2b4de5">pcl::SampleConsensus::~SampleConsensus</a></div><div class="ttdeci">virtual ~SampleConsensus()</div><div class="ttdoc">Destructor for base SAC.</div><div class="ttdef"><b>Definition:</b> sac.h:134</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a1a101dfdcc9098f463db135a7655a438"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a1a101dfdcc9098f463db135a7655a438">pcl::SampleConsensus::getSampleConsensusModel</a></div><div class="ttdeci">SampleConsensusModelPtr getSampleConsensusModel() const</div><div class="ttdoc">Get the Sample Consensus model used.</div><div class="ttdef"><b>Definition:</b> sac.h:128</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a3860965324830148970ba99223663aa2"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a3860965324830148970ba99223663aa2">pcl::SampleConsensus::rng_alg_</a></div><div class="ttdeci">boost::mt19937 rng_alg_</div><div class="ttdoc">Boost-based random number generator algorithm.</div><div class="ttdef"><b>Definition:</b> sac.h:334</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a471e062f42e9cb4ae9d77107cc135acb"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a471e062f42e9cb4ae9d77107cc135acb">pcl::SampleConsensus::iterations_</a></div><div class="ttdeci">int iterations_</div><div class="ttdoc">Total number of internal loop iterations that we've done so far.</div><div class="ttdef"><b>Definition:</b> sac.h:325</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a56b0649ebe9cd4b8a48442f864f5e83c"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a56b0649ebe9cd4b8a48442f864f5e83c">pcl::SampleConsensus::getRandomSamples</a></div><div class="ttdeci">void getRandomSamples(const boost::shared_ptr&lt; std::vector&lt; int &gt; &gt; &amp;indices, size_t nr_samples, std::set&lt; int &gt; &amp;indices_subset)</div><div class="ttdoc">Get a set of randomly selected indices.</div><div class="ttdef"><b>Definition:</b> sac.h:280</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a575ab4a3facfdc8e007f427a96798d7d"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a575ab4a3facfdc8e007f427a96798d7d">pcl::SampleConsensus::getDistanceThreshold</a></div><div class="ttdeci">double getDistanceThreshold()</div><div class="ttdoc">Get the distance to model threshold, as set by the user.</div><div class="ttdef"><b>Definition:</b> sac.h:144</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a6bb9db27c2f0226aaa1e0c2af2b3439e"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a6bb9db27c2f0226aaa1e0c2af2b3439e">pcl::SampleConsensus::computeModel</a></div><div class="ttdeci">virtual bool computeModel(int debug_verbosity_level=0)=0</div><div class="ttdoc">Compute the actual model. Pure virtual.</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a7a731d68a379a0a6442deae93e85d3a8"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a7a731d68a379a0a6442deae93e85d3a8">pcl::SampleConsensus::rnd</a></div><div class="ttdeci">double rnd()</div><div class="ttdoc">Boost-based random number generator.</div><div class="ttdef"><b>Definition:</b> sac.h:341</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a7ac2013afb3a2feaaeb661f3aa3ccf6b"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a7ac2013afb3a2feaaeb661f3aa3ccf6b">pcl::SampleConsensus::getInliers</a></div><div class="ttdeci">void getInliers(std::vector&lt; int &gt; &amp;inliers)</div><div class="ttdoc">Return the best set of inliers found so far for this model.</div><div class="ttdef"><b>Definition:</b> sac.h:300</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a96f852dfca500689684313d3cb7f84b1"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a96f852dfca500689684313d3cb7f84b1">pcl::SampleConsensus::model_coefficients_</a></div><div class="ttdeci">Eigen::VectorXf model_coefficients_</div><div class="ttdoc">The coefficients of our model computed directly from the model found.</div><div class="ttdef"><b>Definition:</b> sac.h:319</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_a9f55f89ee72539f66f7edc8bcf6ce0c2"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#a9f55f89ee72539f66f7edc8bcf6ce0c2">pcl::SampleConsensus::getModelCoefficients</a></div><div class="ttdeci">void getModelCoefficients(Eigen::VectorXf &amp;model_coefficients)</div><div class="ttdoc">Return the model coefficients of the best model found so far.</div><div class="ttdef"><b>Definition:</b> sac.h:306</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_aa1c52d7d8be8f058feac1f9241bf305e"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#aa1c52d7d8be8f058feac1f9241bf305e">pcl::SampleConsensus::threshold_</a></div><div class="ttdeci">double threshold_</div><div class="ttdoc">Distance to model threshold.</div><div class="ttdef"><b>Definition:</b> sac.h:328</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_aa23f804b4957312659adca2068e05682"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#aa23f804b4957312659adca2068e05682">pcl::SampleConsensus::rng_</a></div><div class="ttdeci">boost::shared_ptr&lt; boost::uniform_01&lt; boost::mt19937 &gt; &gt; rng_</div><div class="ttdoc">Boost-based random number generator distribution.</div><div class="ttdef"><b>Definition:</b> sac.h:337</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_aa4953d080c1ab4223cde8ff8d8cabc52"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#aa4953d080c1ab4223cde8ff8d8cabc52">pcl::SampleConsensus::sac_model_</a></div><div class="ttdeci">SampleConsensusModelPtr sac_model_</div><div class="ttdoc">The underlying data model used (i.e. what is it that we attempt to search for).</div><div class="ttdef"><b>Definition:</b> sac.h:310</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_aa5c7f23a52dc184d8cc56790d63d375a"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#aa5c7f23a52dc184d8cc56790d63d375a">pcl::SampleConsensus::getMaxIterations</a></div><div class="ttdeci">int getMaxIterations()</div><div class="ttdoc">Get the maximum number of iterations, as set by the user.</div><div class="ttdef"><b>Definition:</b> sac.h:154</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_aac4937e9bc2a8acf15ae97c7d763090a"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#aac4937e9bc2a8acf15ae97c7d763090a">pcl::SampleConsensus::SampleConsensus</a></div><div class="ttdeci">SampleConsensus(const SampleConsensusModelPtr &amp;model, double threshold, bool random=false)</div><div class="ttdoc">Constructor for base SAC.</div><div class="ttdef"><b>Definition:</b> sac.h:96</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_ab5ca8dbf21b2a1c6ed9c1e8d3eba853c"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#ab5ca8dbf21b2a1c6ed9c1e8d3eba853c">pcl::SampleConsensus::max_iterations_</a></div><div class="ttdeci">int max_iterations_</div><div class="ttdoc">Maximum number of iterations before giving up.</div><div class="ttdef"><b>Definition:</b> sac.h:331</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_aca6f09bf3c664bfed2ae36e81909e9f2"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#aca6f09bf3c664bfed2ae36e81909e9f2">pcl::SampleConsensus::setSampleConsensusModel</a></div><div class="ttdeci">void setSampleConsensusModel(const SampleConsensusModelPtr &amp;model)</div><div class="ttdoc">Set the Sample Consensus model to use.</div><div class="ttdef"><b>Definition:</b> sac.h:121</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_acd6b8031622746d8b6aada91ffbaa7ee"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#acd6b8031622746d8b6aada91ffbaa7ee">pcl::SampleConsensus::setProbability</a></div><div class="ttdeci">void setProbability(double probability)</div><div class="ttdoc">Set the desired probability of choosing at least one sample free from outliers.</div><div class="ttdef"><b>Definition:</b> sac.h:161</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_ad113bbc7fe758479a315bc5108324336"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#ad113bbc7fe758479a315bc5108324336">pcl::SampleConsensus::SampleConsensus</a></div><div class="ttdeci">SampleConsensus()</div><div class="ttdoc">Constructor for base SAC.</div><div class="ttdef"><b>Definition:</b> sac.h:62</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_ae09f01cda7605910955b0aee847ea849"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#ae09f01cda7605910955b0aee847ea849">pcl::SampleConsensus::getModel</a></div><div class="ttdeci">void getModel(std::vector&lt; int &gt; &amp;model)</div><div class="ttdoc">Return the best model found so far.</div><div class="ttdef"><b>Definition:</b> sac.h:294</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_ae1a06ccc992dfc9e65e70f5876f3c8d3"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#ae1a06ccc992dfc9e65e70f5876f3c8d3">pcl::SampleConsensus::setDistanceThreshold</a></div><div class="ttdeci">void setDistanceThreshold(double threshold)</div><div class="ttdoc">Set the distance to model threshold.</div><div class="ttdef"><b>Definition:</b> sac.h:140</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_aee8d85e0b1062f5e18d43609e6ac59bf"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#aee8d85e0b1062f5e18d43609e6ac59bf">pcl::SampleConsensus::SampleConsensus</a></div><div class="ttdeci">SampleConsensus(const SampleConsensusModelPtr &amp;model, bool random=false)</div><div class="ttdoc">Constructor for base SAC.</div><div class="ttdef"><b>Definition:</b> sac.h:72</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_af7c059e9ee5b5180bb7fb02b0d947c36"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#af7c059e9ee5b5180bb7fb02b0d947c36">pcl::SampleConsensus::refineModel</a></div><div class="ttdeci">virtual bool refineModel(const double sigma=3.0, const unsigned int max_iterations=1000)</div><div class="ttdoc">Refine the model found. This loops over the model coefficients and optimizes them together with the s...</div><div class="ttdef"><b>Definition:</b> sac.h:179</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_af8558bc2462b6da4a2f88b2efc1ad571"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#af8558bc2462b6da4a2f88b2efc1ad571">pcl::SampleConsensus::setMaxIterations</a></div><div class="ttdeci">void setMaxIterations(int max_iterations)</div><div class="ttdoc">Set the maximum number of iterations.</div><div class="ttdef"><b>Definition:</b> sac.h:150</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_html_afafb66faf0cbfa464cd884127bafdac3"><div class="ttname"><a href="classpcl_1_1_sample_consensus.html#afafb66faf0cbfa464cd884127bafdac3">pcl::SampleConsensus::getProbability</a></div><div class="ttdeci">double getProbability()</div><div class="ttdoc">Obtain the probability of choosing at least one sample free from outliers, as set by the user.</div><div class="ttdef"><b>Definition:</b> sac.h:165</div></div>
<div class="ttc" id="aclasspcl_1_1_sample_consensus_model_html"><div class="ttname"><a href="classpcl_1_1_sample_consensus_model.html">pcl::SampleConsensusModel</a></div><div class="ttdoc">SampleConsensusModel represents the base model class. All sample consensus models must inherit from t...</div><div class="ttdef"><b>Definition:</b> sac_model.h:67</div></div>
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